INTRODUCTION

Stroke is among the leading causes of death and disability in Thailand. The prevalence of stroke is estimated to be 1.88% among adults 45 years and older [1]. In Singapore, the stroke prevalence was 7.6%, (95% confidence interval [CI], 6.2 to 9.0) among older adult residents aged 60 years and above, which is relatively high when compared with other Asian countries [2]. After being discharged from the hospital, most stroke survivors return home and live with their families, but often have long-term difficulties with walking and other daily activities [3,4]. Several studies have shown that the mobility and functional dependence of stroke survivors worsened over time [5,6]. With developments in primary health and home care systems, most people with stroke are allowed to stay in the hospital for only a short period. Therefore, increasingly many patients will directly return to their homes and communities faster. Regarding this shift in service delivery, the key role of community physical therapy (PT) in stroke rehabilitation will be increasingly emphasized. The goals of community PT for people after stroke are promoting self-care and functional independence, training disabled people to move around, and optimizing patients’ participation in society [7,8]. Regaining community mobility is the primary goal for people with stroke during rehabilitation, since it is a key factor in becoming independent in daily functioning [9,10].

In assessing community mobility, different aspects of the environment need to be considered. Patla and Shumway-Cook [11] published a conceptual model outlining 8 dimensions of mobility. In a subsequent study, wherein the mobility in the community of people with disabilities was observed and compared to that of people without disabilities, 4 dimensions were indicated as being important, including terrain (such as stairs, slopes, uneven surfaces, and obstacles), temporal factors (walking speed), postural transitions, and physical loads [9]. Corrigan and McBurney [12] reviewed the assessment tools that are currently used by PTs and found that the most commonly measured dimensions were distance and terrain, while the impact of the environment in current assessment tools is limited in clinical usefulness.

The most common outcome measure for people with stroke utilized in community-based rehabilitation programs in Thailand is the Barthel Index (BI). The BI is designed to assess a patient’s ability to perform simple self-care activities. It does not include items for measuring more complex activities related to mobility, which are necessary for an independent lifestyle after stroke in community settings. In addition, the dichotomous scale of the BI reduces outcome information and may limit the scale’s ability to detect a significant shift in disability [13,14]. Other assessment scales such as the Motor Assessment Scale, the Rivermead Mobility Index (RMI), and the Functional Independence Measure are also commonly used to assess mobility following stroke [15]. These assessment tools have limitations for use in community settings, such as (1) not covering the environmental dimensions of mobility, (2) focusing on measures in hospital-based care rather than in home or community settings [12,16], and (3) being time-consuming for routine use. The RMI is a tool for assessing mobility in stroke patients in home settings. However, its dichotomous scoring system may limit its applicability in a community setting [17]. Therefore, RMI was modified to extend the scoring system; the resultant modified Rivermead Mobility Index (MRMI) showed high reliability between raters, with high internal consistency [18]. However, the MRMI was recommended to measure mobility in the acute and sub-acute stroke phase [19], since the MRMI score did not show meaningful changes at 90 days to 180 days after stroke onset [20].

There is a need to develop a scale that is simple but specific and suitable for measuring mobility in the community setting for people with stroke. Furthermore, a new scale should be designed with a detailed scoring system covering a wide spectrum of patients’ abilities, ranging from basic sitting to a level of mobility that would allow them to participate in society. Therefore, the aim of this study was to develop the Mobility to Participation Assessment Scale for Stroke (MPASS) and to examine the psychometric properties of the tool, including internal consistency of the items, inter-rater reliability, intra-rater reliability, and convergent validity.

METHODS

This study was performed in 2 phases: development of the MPASS and evaluation of its psychometric properties.

Phase I: Development of the MPASS

The development of the MPASS was partially based on the COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) guideline [21], because MPASS was intended to be a performance-based outcome measure (i.e., not solely as a patient-reported outcome measure [PROM]); therefore, only some of the COSMIN criteria were applied to develop the MPASS. Following the general design requirements of the COSMIN outcome measure guideline, the construction, target population, and context of use of MPASS were developed, as shown in Table 1.

After laying out the concept of creating the MPASS, we collected the data needed to generate its items. Initial items were chosen by searching for instruments used to assess the mobility of post-stroke patients in the literature, focusing on movement that progressed to participation in society and using a questionnaire to elicit opinions from community PTs on the mobility tasks that are necessary in community settings. The questionnaire was sent to 100 community PTs who were recruited from primary health care units and community hospitals covering all regions of Thailand. For qualitative data obtained through a survey questionnaire, a sample size ≥100 is considered very good for general design requirements according to the COSMIN recommendations [21]. The inclusion criteria for community PTs were a minimum of 2 years’ experience in community stroke rehabilitation and currently practicing in the field. The data from the questionnaire and the literature review were integrated to develop the first draft of the MPASS.

Next, a focus group that consisted of 5 community PTs and 3 professional experienced PTs was arranged to discuss the content and constructs of the MPASS according to COSMIN. The results and the recommendations from the focus group were used to revise the drafted MPASS. After revision, this scale was sent to the participants in the previous focus group to confirm its relevancy, clarity, and applicability. The content validity of the revised version was determined by 3 PT experts who had at least 5 years of experience using the item-objective congruence (IOC) index. An IOC greater than 0.5 was accepted for the final version.

Phase II: Reliability Testing and Other Psychometric Properties of the MPASS

The main aim of this phase was to examine internal consistency, which is important in multiple-item scales because it indicates whether all items measure a similar construct. Moreover, the inter-rater reliability, intra-rater reliability, and convergent validity were used to study other psychometric properties. This phase was conducted among people with stroke who had been discharged from the hospital. People with stroke were included in the study if they met the following criteria: (1) diagnosis of stroke, (2) first onset of stroke and the absence of pre-existing disability, (3) a BI ≥40, (4) ability to follow instructions as screened by a score ≥24 on the Thai Mini-Mental State Examination, (5) a medically stable condition, and (6) willingness to participate in this study.

The sample size calculation for this study was based on a power of 0.80 and alpha level of 0.05. This study aimed to develop a new scale; thus, the expected correlation coefficient (r) for the new scale was set at 0.80, which represented an acceptable correlation, whereas the r-value for the null hypothesis was set to be at least 0.5, representing an adequate correlation. The sample size for reliability and validity testing in this study was determined to be at least 29 participants. The dropout rate was calculated at 10%. Therefore, a sample size of 32 was selected.

The procedure of evaluating inter-rater and intra-rater reliability [22,23] was determined by 3 PTs who were recruited from community settings. The raters were recruited based on the following inclusion criteria: (1) a minimum of 2 years’ experience in community stroke rehabilitation and (2) currently practicing in the field. Prior to the study, all raters were provided with a copy of the criteria for scoring and general rules and given a period of 2 weeks to practice using the MPASS. After the practice period, the raters were asked to use the MPASS to score each patient’s performance based on videotapes. During the rating session, the raters were unaware of each other’s scores, and they were not allowed to discuss the patient’s performance. The data were used to analyze inter-rater reliability by comparing the MPASS score assigned by all raters for each patient. One week later, the videotaped MPASS assessments were shown to the raters, who rated the patients again to determine intra-rater reliability.

Convergent validity was determined by the same 3 raters. All raters scored the patients with stroke performance using the MPASS and the MRMI. The MRMI, which uses the same constructs as the MPASS to measure disability related to mobility, showed acceptable levels of reliability, validity, and responsiveness in people with stroke. It consists of 8 items: turning over, lying to sitting, sitting balance, sitting to standing, standing, transfers, walking indoors, and stairs. The scores of the MRMI range from 0 to 5, indicating functional ability on a spectrum from unable to perform to requiring assistance and independent [18]. The scores of the MPASS and MRMI were used to analyze convergent validity by examining the relationship between the total scores of both scales.

Statistical Analysis

Internal consistency was analyzed using the item-total correlation and the Cronbach alpha coefficient. The item-total correlation represents the correlation between each item and the entire score of MPASS. The corrected item-total correlation ranges from −1.00 to +1.00, and a value of +0.30 or more represents a good correlation [24]. The Cronbach alpha is generally considered acceptable if it is greater than 0.7 and good if it is greater than 0.8 [22].

The intraclass correlation coefficient (ICC) was used to analyze inter-rater and intra-rater reliability. The ICC model (2, 1) was used to compute the inter-rater reliability and the ICC model (3, 1) was used to compute the intra-rater reliability. ICC values of 0.8 and above indicate an excellent correlation (good reliability), values of 0.6–0.8 indicate an adequate correlation (moderate reliability), and values of 0.4–0.6 indicate a poor correlation (weak reliability). Finally, convergent validity was analyzed using the Spearman rank-order correlation coefficient, with values of 0.80 or greater indicating an excellent correlation, 0.50 to 0.79 indicating a moderate correlation, and 0.00 to 0.49 indicating a poor correlation [22].

Ethics Statement

The study was approved by the Ethics Committee of the Physical Therapy Faculty, Srinakharinwirot University, Thailand (HSPT2014-007), and the participants provided written informed consent.

RESULTS

Phase I: Development of the MPASS

Eighty-one out of 100 community PTs (81.0%) completed the questionnaire. They reported that sitting was the most frequently identified ability found in community-dwelling people with stroke at their first visit (46.9%). Therefore, the MPASS was designed to start from the sitting position. Consensus from the focus group of 8 participants together, with the results from questionnaires, reduced the number evaluated items from 14 to 8. The revised scale was sent to the participants in the focus group to confirm its relevancy, clarity, and applicability. The content validity of the revised version showed that the content of the MPASS was acceptable, as represented by an IOC for each item greater than 0.50 (range, 0.67–1.00). The 8 items of the final version and the maximal score description for each item are shown in Table 2. Each item is scored 0–4, and the total score of the MPASS is 32. The scoring descriptors of MPASS are as follows: 0 means moving with assistance or being dependent, 1 means being able to move independently, 2 means able to move independently in a suitable time, 3 means being able to move independently in a suitable time when changing posture, and 4 means being able to move independently to participate in daily living in a suitable time.

Phase II: Reliability Testing and Other Psychometric Properties of the MPASS

Internal consistency

Each item of the MPASS showed a good correlation with the overall score of the MPASS (range, 0.408–0.818). The Cronbach alpha of the MPASS was 0.877, corresponding to good internal consistency, as shown in Table 3.

Reliability

The intra-rater ICC of the overall MPASS was 0.967 (95% CI, 0.933 to 0.989), and the item-level ICCs ranged from 0.962 to 1.000. The inter-rater ICC of the overall MPASS was 0.948 (95% CI, 0.893 to 0.982), and the item-level ICCs ranged from 0.948 to 1.000, as shown in Table 4.

Convergent validity

The convergent validity of the MPASS was analyzed using the correlation of the total score of MPASS with the MRMI, which was assessed in 32 people with stroke. The average score of the MPASS was 20.24±3.09 out of a total score of 32, and the average score of the MRMI was 37.70±1.51 out of a total score of 40. The correlation coefficient between the MPASS and MRMI was 0.646 (p<0.001), indicating moderate convergent validity.

DISCUSSION

This study was conducted to develop the MPASS to assess mobility in people with stroke who are living in the community. The MPASS was partially developed based on the COSMIN Risk of Bias guideline for evaluating the methodological quality of measurement properties [21]. The MPASS development followed the 3-step procedure of the COSMIN checklist: content validity, internal structure, and remaining measurement properties. The COSMIN checklist was developed to assess the methodological quality of PROMs. A PROM is a measurement of any aspect of a patient’s health status that is directly assessed by the patient. Modes of data collection for PROM instruments include interviewer-administered, self-administered, and computer-administered instruments. However, the MPASS is a performance-based measure that assesses actual performance in a particular environment at a specific point in time; therefore, some COSMIN checklist items were not applied in the MPASS development process.

The MPASS was developed based on the concepts of activity and participation according to the World Health Organization International Classification of Functioning, Disability, and Health framework, corresponding to the environmental factors related to mobility concepts reported by Shumway-Cook et al. [9]. The 4 dimensions of environmental factors that affect a person with stroke include time constraints, as assessed in terms of walking speed (MPASS item 5); terrain characteristics with different geometric properties such as steps (MPASS items 4 and 8); external physical loads such as objects that are carried (MPASS item 7); and postural transitions such as changes in position or direction (MPASS items 1–3). Moreover, the attentional demand dimension of environmental factors, such as walking with distractions (MPASS item 6), was added to clinical usefulness as an indicator of fall risk in identifying the community-dwelling people with stroke who are most at risk of falls and in need of therapeutic intervention [25,26]. The MPASS was designed to have a scoring system that covers a wide spectrum of patients’ abilities, ranging from basic sitting to a level of mobility that enables them to participate in society. A high score indicates that a person with stroke is able to move sufficiently to participate in daily living in a community context with appropriate time and/or external loads. This scale differs from previous scales that only assess the ability to move (i.e., whether a person can or cannot perform a given movement).

The ability to integrate walking with other tasks in a complex environment (i.e., community ambulation) is an important goal for people with stroke [12,27]. The recovery of walking ability in the community has most often been studied among patients after stroke, and the inability to leave one’s home and reduced levels of community walking have been linked to poorer quality of life in this population [27]. In 2005, Viosca et al. [28] showed that gait recovery mainly occurred in the majority of patients during the first 3 months after stroke, while the functional level improved in 37% of patients after 8 months. Therefore, the MPASS consists of 5 items (62.5%) that mainly assess walking activity consisting of stepping over obstacles, walking with one’s preferred speed, walking while talking, walking with carrying objects, and moving up/down stairs. A previous study showed that mobility abilities influenced the physical health component of health-related quality of life (HRQoL), including physical function and general health (HRQoL) [29], and mobility problems were also dimension-specific factors influencing HRQoL in people with stroke, the importance of which increased with old age [30]. Higher levels of mobility and participation in society would lead to better HRQoL [31]. The MPASS could be used to assess patients’ mobility and participation level, which may reflect their quality of life. Further research on the association of MPASS with HRQoL will clarify the importance of community mobility levels in people with stroke.

The MPASS showed satisfactory results in terms of intra-rater and inter-rater reliability (ICC>0.90), according to Portney and Watkins [22]’ recommendation that the ICC for a clinical measure should exceed 0.90 before the test can be deemed reliable. The moderate convergent validity of the MPASS as compared to the MRMI reflects the use of similar mobility construct outcome measures. Although the MRMI is a measure of the patient’s ability to perform an activity independently [18], the MPASS also assesses the quality of the patient’s performance related to community mobility. For example, a score of 4 of MPASS item 5 (walking speed on the ground) means walking at a speed of 1.2 m/sec, which reflects the generally accepted speed of walking across a road [32]. Therefore, the moderate correlation may reflect the similarity of constructs, but not of the scoring system.

The MPASS was found to be suitable for assessing patients in the subacute stage following stroke who initially have the ability to sit. The present study confirmed that the MPASS is reliable, valid, and simple, and even more importantly, that the MPASS targets items that are relevant to the aims of therapists working in community stroke rehabilitation. The MPASS could be used as a specific assessment instrument for community mobility and assistance in rehabilitation management planning. Furthermore, since the MPASS requires no equipment, and the scoring items are relevant to community mobility, it is also simple enough to be used by community healthcare personnel.

The main limitation of this study could be the comprehensibility of the MPASS. The MPASS was developed with the aim of being used by PTs or community healthcare workers, but in the initial process of development, community healthcare workers and people with stroke were not included. To further improve the applicability of the MPASS, more stakeholders should be considered. In addition, responsiveness, as well as floor and ceiling effects, should be established to evaluate the degree to which the MPASS can be used to detect changes in people with stroke’s performance. Finally, the MPASS was developed in the Thai context. Therefore, cross-culturally appropriate translations of the MPASS would be important for its implementation in a global context.

Notes

CONFLICT OF INTEREST

The authors have no conflicts of interest associated with the material presented in this paper.

FUNDING

This study was partially funded by The National Health Security Office, Thailand.

ACKNOWLEDGEMENTS

Notes

AUTHOR CONTRIBUTIONS

Both authors contributed equally to conceiving the study, analyzing the data, and writing this paper.

REFERENCES

• 1. Hanchaiphiboolkul S, Poungvarin N, Nidhinandana S, Suwanwela NC, Puthkhao P, Towanabut S, et al. Prevalence of stroke and stroke risk factors in Thailand: Thai Epidemiologic Stroke (TES) Study. J Med Assoc Thai 2011;94(4):427-436PubMed
• 2. Teh WL, Abdin E, Vaingankar JA, Seow E, Sagayadevan V, Shafie S, et al. Prevalence of stroke, risk factors, disability and care needs in older adults in Singapore: results from the WiSE study. BMJ Open 2018;8(3):e020285ArticlePubMedPMC
• 3. Thorngren M, Westling B, Norrving B. Outcome after stroke in patients discharged to independent living. Stroke 1990;21(2):236-240ArticlePubMed
• 4. McKenna K, Tooth L, Strong J, Ottenbacher K, Connell J, Cleary M. Predicting discharge outcomes for stroke patients in Australia. Am J Phys Med Rehabil 2002;81(1):47-56ArticlePubMed
• 5. Gilbertson L, Langhorne P, Walker A, Allen A, Murray GD. Domiciliary occupational therapy for patients with stroke discharged from hospital: randomised controlled trial. BMJ 2000;320(7235):603-606ArticlePubMedPMC
• 6. Brauer SG, Bew PG, Kuys SS, Lynch MR, Morrison G. Prediction of discharge destination after stroke using the motor assessment scale on admission: a prospective, multisite study. Arch Phys Med Rehabil 2008;89(6):1061-1065ArticlePubMed
• 7. Twible RL, Henley EC. Preparing occupational therapists and physiotherapists for community based rehabilitation. Asia Pac Disabil Rehabil J 2000;113-129
• 8. Bury T. Primary health care and community based rehabilitation: implication for physical therapy. Asia Pac Disabil Rehabil J 2005;16(2):29-61
• 9. Shumway-Cook A, Patla AE, Stewart A, Ferrucci L, Ciol MA, Guralnik JM. Environmental demands associated with community mobility in older adults with and without mobility disabilities. Phys Ther 2002;82(7):670-681ArticlePubMedPDF
• 10. van de Port IG, Kwakkel G, Lindeman E. Community ambulation in patients with chronic stroke: how is it related to gait speed? J Rehabil Med 2008;40(1):23-27ArticlePubMed
• 11. Patla AE, Shumway-Cook A. Dimensions of mobility: defining the complexity and difficulty associated with community mobility. J Aging Phys Act 1999;7(1):7-19Article
• 12. Corrigan R, McBurney H. Community ambulation: environmental impacts and assessment inadequacies. Disabil Rehabil 2008;30(19):1411-1419ArticlePubMed
• 13. Hsueh IP, Lin JH, Jeng JS, Hsieh CL. Comparison of the psychometric characteristics of the functional independence measure, 5 item Barthel index, and 10 item Barthel index in patients with stroke. J Neurol Neurosurg Psychiatry 2002;73(2):188-190ArticlePubMedPMC
• 14. Duncan PW, Jorgensen HS, Wade DT. Outcome measures in acute stroke trials: a systematic review and some recommendations to improve practice. Stroke 2000;31(6):1429-1438ArticlePubMed
• 15. Salter K, Jutai JW, Teasell R, Foley NC, Bitensky J, Bayley M. Issues for selection of outcome measures in stroke rehabilitation: ICF activity. Disabil Rehabil 2005;27(6):315-340ArticlePubMed
• 16. Sabari JS, Lim AL, Velozo CA, Lehman L, Kieran O, Lai JS. Assessing arm and hand function after stroke: a validity test of the hierarchical scoring system used in the motor assessment scale for stroke. Arch Phys Med Rehabil 2005;86(8):1609-1615ArticlePubMed
• 17. Lennon S, Hastings M. Key physiotherapy indicators for quality of stroke care. Physiotherapy 1996;82(12):655-664Article
• 18. Lennon S, Johnson L. The modified rivermead mobility index: validity and reliability. Disabil Rehabil 2000;22(18):833-839ArticlePubMed
• 19. Johnson L, Selfe J. Measurement of mobility following stroke: a comparison of the Modified Rivermead Mobility Index and the Motor Assessment Scale. Physiotherapy 2004;90(3):132-138Article
• 20. Hsueh IP, Wang CH, Sheu CF, Hsieh CL. Comparison of psychometric properties of three mobility measures for patients with stroke. Stroke 2003;34(7):1741-1745ArticlePubMed
• 21. Mokkink LB, de Vet HC, Prinsen CA, Patrick DL, Alonso J, Bouter LM, et al. COSMIN risk of bias checklist for systematic reviews of patient-reported outcome measures. Qual Life Res 2018;27(5):1171-1179ArticlePubMedPMCPDF
• 22. Portney LG, Watkins MP. Foundations of clinical research: applications to practice. 3rd ed. London: Pearson Prentice Hall; 2008. p. 585-618
• 23. Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 2016;15(2):155-163ArticlePubMedPMC
• 24. Boateng GO, Neilands TB, Frongillo EA, Melgar-Quiñonez HR, Young SL. Best practices for developing and validating scales for health, social, and behavioral research: a primer. Front Public Health 2018;6: 149ArticlePubMedPMC
• 25. Hyndman D, Ashburn A. Stops walking when talking as a predictor of falls in people with stroke living in the community. J Neurol Neurosurg Psychiatry 2004;75(7):994-997ArticlePubMedPMC
• 26. Hajek VE, Gagnon S, Ruderman JE. Cognitive and functional assessments of stroke patients: an analysis of their relation. Arch Phys Med Rehabil 1997;78(12):1331-1337ArticlePubMed
• 27. Lord SE, Rochester L. Measurement of community ambulation after stroke: current status and future developments. Stroke 2005;36(7):1457-1461ArticlePubMed
• 28. Viosca E, Lafuente R, Martínez JL, Almagro PL, Gracia A, González C. Walking recovery after an acute stroke: assessment with a new functional classification and the Barthel Index. Arch Phys Med Rehabil 2005;86(6):1239-1244ArticlePubMed
• 29. Jung H, Tanaka S, Iwamoto Y, Yamasaki M, Tanaka R. Relationship between mobility-related activities of daily living and health-related quality of life among healthy older adults: a cross-sectional study using structural equation modeling. Gerontol Geriatr Med 2021;7: 23337214211013166ArticlePDF
• 30. Kwon S, Park JH, Kim WS, Han K, Lee Y, Paik NJ. Health-related quality of life and related factors in stroke survivors: data from Korea National Health and Nutrition Examination Survey (KNHANES) 2008 to 2014. PLoS One 2018;13(4):e0195713ArticlePubMedPMC
• 31. Lynch EB, Butt Z, Heinemann A, Victorson D, Nowinski CJ, Perez L, et al. A qualitative study of quality of life after stroke: the importance of social relationships. J Rehabil Med 2008;40(7):518-523ArticlePubMedPMC
• 32. Asher L, Aresu M, Falaschetti E, Mindell J. Most older pedestrians are unable to cross the road in time: a cross-sectional study. Age Ageing 2012;41(5):690-694ArticlePubMed

Figure & Data

References

Citations

Citations to this article as recorded by